Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Control of synaptic plasticity in deep cortical networks

Key Points

  • In addition to presynaptic and postsynaptic mechanisms, synaptic plasticity depends on neuromodulatory substances and feedback connections from higher-order cortical and thalamic brain regions

  • Synaptic plasticity in the brain depends on reward-prediction errors and on selective attention. Neuromodulatory systems code for the reward-prediction errors, and feedback connections from the response-selection stage mediate top-down attention effects

  • The combined influence of feedback connections and neuromodulatory substances on plasticity enables powerful learning rules for the training of 'deep', multilayered neuronal networks

  • Feedback connections project to cortical layers that are distinct from feedforward input, where they impinge on distal dendritic segments, separate excitatory neuronal populations or inhibitory interneurons

  • Feedback connections gate plasticity in cortical pyramidal neurons by promoting NMDA-receptor-driven calcium entry into dendrites and by disinhibiting the cortical column through activation of vasoactive-intestinal-peptide-positive interneurons (among others)

  • Synaptic tags are biochemical processes that make synapses eligible for plasticity. Neuromodulators released later can interact with tagged synapses to increase or decrease synaptic strength

Abstract

Humans and many other animals have an enormous capacity to learn about sensory stimuli and to master new skills. However, many of the mechanisms that enable us to learn remain to be understood. One of the greatest challenges of systems neuroscience is to explain how synaptic connections change to support maximally adaptive behaviour. Here, we provide an overview of factors that determine the change in the strength of synapses, with a focus on synaptic plasticity in sensory cortices. We review the influence of neuromodulators and feedback connections in synaptic plasticity and suggest a specific framework in which these factors can interact to improve the functioning of the entire network.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Putative control signals that influence synaptic plasticity.
Figure 2: Cortical feedforward, feedback and neuromodulatory information streams.
Figure 3: Effects of learning on neuronal tuning curves.
Figure 4: Attentional selection and eye movement selection during curve tracing.
Figure 5: Gating of plasticity of feedforward connections to the primary somatosensory cortex.
Figure 6: Gating and steering of synaptic plasticity.

References

  1. 1

    Sutton, R. S. & Barto, A. G. Reinforcement Learning (MIT Press, 1998).

    Google Scholar 

  2. 2

    Littman, M. L. Reinforcement learning improves behaviour from evaluative feedback. Nature 521, 445–451 (2015).

    CAS  Article  PubMed  Google Scholar 

  3. 3

    Rumelhart, D. E., Hinton, G. E. & Williams, R. J. in Parallel Distributed Processing: Explorations in the Microstructure of Cognition (eds Rumelhart, D. E. & McClelland, J. L.) 318–364 (MIT Press, 1986).

    Google Scholar 

  4. 4

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS  Article  PubMed  Google Scholar 

  5. 5

    Hebb, D. O. The Organization of Behavior. A Neuropsychological Theory (John Wiley & Sons, 1949).

    Google Scholar 

  6. 6

    Martin, S. J., Grimwood, P. D. & Morris, R. G. M. Synaptic plasticity and memory: an evaluation of the hypothesis. Annu. Rev. Neurosci. 23, 649–711 (2000).

    CAS  Article  PubMed  Google Scholar 

  7. 7

    Schultz, W. Getting formal with dopamine and reward. Neuron 36, 241–263 (2002).

    CAS  Article  PubMed  Google Scholar 

  8. 8

    Niv, Y. & Schoenbaum, G. Dialogues on prediction errors. Trends Cogn. Sci. 12, 265–272 (2008).

    Article  PubMed  Google Scholar 

  9. 9

    Baxter, J. & Bartlett, P. L. Infinite-horizon policy-gradient estimation. J. Artif. Intell. Res. 15, 319–350 (2001).

    Article  Google Scholar 

  10. 10

    Frémaux, N., Sprekeler, H. & Gerstner, W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput. Biol. 9, e1003024 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Izhikevich, E. M. Solving the distal reward problem through linkage of STDP and dopamine signalling. Cereb. Cortex 17, 2443–2452 (2007).

    Article  PubMed  Google Scholar 

  12. 12

    Legenstein, R., Pecevski, D. & Maass, W. A learning theory for reward-modulated spike-timing-dependent plasticity with application in biofeedback. PLoS Comput. Biol. 4, e1000180 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Rombouts, J. O., Bohte, S. M., Martinez-trujillo, J., Roelfsema, P. R. & Pieter, R. A learning rule that explains how rewards teach attention. Vis. Cogn. 23, 179–205 (2015).

    Article  Google Scholar 

  14. 14

    Yagishita, S. et al. A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science 345, 1616–1620 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15

    Frey, U. & Morris, R. G. M. Synaptic tagging and long-term potentiation. Nature 385, 533–536 (1997).

    CAS  Article  PubMed  Google Scholar 

  16. 16

    Montague, P. R., Dayan, P., Person, C. & Sejnowski, T. J. Bee foraging in uncertain environments using predictive Hebbian learning. Nature 377, 725–728 (1995).

    CAS  Article  PubMed  Google Scholar 

  17. 17

    Lisman, J., Grace, A. A. & Duzel, E. A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP. Trends Neurosci. 34, 536–547 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18

    Frémaux, N. & Gerstner, W. Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front. Neural Circuits 9, 85 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19

    Pennartz, C. A. M. The ascending neuromodulatory systems in learning by reinforcement: comparing computational conjectures with experimental findings. Brain Res. Rev. 21, 219–245 (1995).

    CAS  Article  PubMed  Google Scholar 

  20. 20

    Trabasso, T. & Bower, G. H. Attention in Learning: Theory and Research (Krieger Pub. Co., 1968).

    Google Scholar 

  21. 21

    Ahissar, M. & Hochstein, S. Attentional control of early perceptual learning. Proc. Natl Acad. Sci. USA 90, 5718–5722 (1993).

    CAS  Article  PubMed  Google Scholar 

  22. 22

    Jiang, Y. & Chun, M. M. Selective attention modulates implicit learning. Q. J. Exp. Psychol. 54, 1105–1124 (2001).

    CAS  Article  Google Scholar 

  23. 23

    Vartak, D., Jeurissen, D., Self, M. W. & Roelfsema, P. R. The influence of attention and reward on the learning of stimulus-response associations. Sci. Rep. 7, 9036 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Seitz, A. R., Kim, D. & Watanabe, T. Rewards evoked learning of unconsciously processed visual stimuli in adult humans. Neuron 61, 700–707 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25

    Roelfsema, P. R., van Ooyen, A. & Watanabe, T. Perceptual learning rules based on reinforcers and attention. Trends Cogn. Sci. 14, 64–71 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26

    Jonikaitis, D. & Deubel, H. Independent allocation of attention to eye and hand targets in coordinated eye-hand movements. Psychol. Sci. 22, 339–347 (2011).

    Article  PubMed  Google Scholar 

  27. 27

    Moore, T. Shape representations and visual guidance of saccadic eye movements. Science 285, 1914–1917 (1999).

    CAS  Article  PubMed  Google Scholar 

  28. 28

    Roelfsema, P. R. & van Ooyen, A. Attention-gated reinforcement learning of internal representations for classification. Neural Comp. 17, 2176–2214 (2005).

    Article  Google Scholar 

  29. 29

    Lamme, V. A. F. & Roelfsema, P. R. The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23, 571–579 (2000).

    CAS  Article  PubMed  Google Scholar 

  30. 30

    Moore, T. & Armstrong, K. M. Selective gating of visual signals by microstimulation of frontal cortex. Nature 421, 370–373 (2003).

    CAS  Article  PubMed  Google Scholar 

  31. 31

    Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J. Random synaptic feedback weights support error backpropagation for deep learning. Nat. Commun. 7, 13276 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32

    Rombouts, J. O., Bohte, S. M. & Roelfsema, P. R. How attention can create synaptic tags for the learning of working memories in sequential tasks. PLoS Comput. Biol. 11, e1004060 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    CAS  Article  PubMed  Google Scholar 

  34. 34

    Crick, F. The recent excitement about neural networks. Nature 337, 129–132 (1989).

    CAS  Article  PubMed  Google Scholar 

  35. 35

    Braitenberg, V. & Schütz, A. Anatomy of the Cortex (Springer-Verlag, 1991).

    Google Scholar 

  36. 36

    Mountcastle, V. B. in The Mindful Brain (eds Edelman, G. M. & Mountcastle, V. B.) (MIT Press, 1978).

    Google Scholar 

  37. 37

    Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).

    CAS  Article  PubMed  Google Scholar 

  38. 38

    Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).

    CAS  Article  PubMed  Google Scholar 

  39. 39

    Sherman, S. M. Thalamus plays a central role in ongoing cortical functioning. Nat. Neurosci. 16, 533–541 (2016).

    Article  CAS  Google Scholar 

  40. 40

    Callaway, E. M. Feedforward, feedback and inhibitory connections in primate visual cortex. Neural Networks 17, 625–632 (2004).

    Article  PubMed  Google Scholar 

  41. 41

    Harris, K. D. & Mrsic-Flogel, T. D. Cortical connectivity and sensory coding. Nature 503, 51–58 (2013).

    CAS  Article  PubMed  Google Scholar 

  42. 42

    Douglas, R. & Martin, K. A. C. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451 (2004).

    CAS  Article  PubMed  Google Scholar 

  43. 43

    Markov, N. T. et al. Cortical high-density counterstream architectures. Science 342, 1238406 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Ullman, S. Sequence seeking and counterstreams: a computational model for bidirectional information flow in the visual cortex. Cereb. Cortex 5, 1–11 (1995).

    CAS  Article  PubMed  Google Scholar 

  45. 45

    Harris, K. D. & Shepherd, G. M. G. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46

    Feldmeyer, D. Excitatory neuronal connectivity in the barrel cortex. Front. Neuroanat. 6, 24 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47

    Maunsell, J. H. R. & Gibson, J. R. Visual response latencies in striate cortex of the macaque monkey. J. Neurophysiol. 68, 1332–1344 (1992).

    CAS  Article  PubMed  Google Scholar 

  48. 48

    Nowak, L. G., Munk, M. H. J., Girard, P. & Bullier, J. Visual latencies in areas V1 and V2 of the macaque monkey. Visual Neurosci. 12, 371–384 (1995).

    CAS  Article  Google Scholar 

  49. 49

    van Kerkoerle, T., Self, M. W. & Roelfsema, P. R. Effects of attention and working memory in the different layers of monkey primary visual cortex. Nat. Commun. 8, 13804 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50

    Self, M. W., van Kerkoerle, T., Supèr, H. & Roelfsema, P. R. Distinct roles of the cortical layers of area V1 in figure-ground segregation. Curr. Biol. 23, 2121–2129 (2013).

    CAS  Article  PubMed  Google Scholar 

  51. 51

    Constantinople, C. M. & Bruno, R. M. Deep cortical layers are activated by thalamus. Science 340, 1591–1594 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52

    Morgenstern, N. A., Bourg, J. & Petreanu, L. Multilaminar networks of cortical neurons integrate common inputs from sensory thalamus. Nat. Neurosci. 19, 1034–1040 (2016).

    CAS  Article  PubMed  Google Scholar 

  53. 53

    Bolz, J. & Gilbert, C. D. Generation of end-inhibition in the visual cortex via interlaminar connections. Nature 320, 362–365 (1986).

    CAS  Article  PubMed  Google Scholar 

  54. 54

    Olsen, S. R., Bortone, D. S., Adesnik, H. & Scanziani, M. Gain control by layer six in cortical circuits of vision. Nature 483, 47–52 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55

    Bortone, D. S., Olsen, S. R. & Scanziani, M. Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex. Neuron 82, 474–485 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56

    Rockland, K. S. & Virga, A. Terminal arbors of individual 'feedback' axons projecting from area V2 to V1 in the macaque monkey: a study using immunohistochochemistry of anterogradely transported Phaseolus vulgaris-leucoagglutinin. J. Comp. Neurol. 285, 54–72 (1989).

    CAS  Article  PubMed  Google Scholar 

  57. 57

    Larkum, M. E. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–149 (2013).

    CAS  Article  PubMed  Google Scholar 

  58. 58

    Schneider, D. M., Nelson, A. & Mooney, R. A synaptic and circuit basis for corollary discharge in the auditory cortex. Nature 513, 189–194 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59

    Lee, S., Kruglikov, I., Huang, Z. J., Fishell, G. & Rudy, B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat. Neurosci. 16, 1662–1670 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60

    van Versendaal, D. & Levelt, C. N. Inhibitory interneurons in visual cortical plasticity. Cell. Mol. Life Sci. 73, 3677–3691 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61

    Deschênes, M., Veinante, P. & Zhang, Z. W. The organization of corticothalamic projections: reciprocity versus parity. Brain Res. Rev. 28, 286–308 (1998).

    Article  PubMed  Google Scholar 

  62. 62

    Veinante, P., Lavallée, P. & Deschênes, M. Corticothalamic projections from layer 5 of the vibrissal barrel cortex in the rat. J. Comp. Neurol. 424, 197–204 (2000).

    CAS  Article  PubMed  Google Scholar 

  63. 63

    Jones, E. G. Thalamus (Cambridge Univ. Press, 2007).

    Google Scholar 

  64. 64

    Roth, M. M. et al. Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex. Nat. Neurosci. 19, 299–307 (2016).

    CAS  Article  PubMed  Google Scholar 

  65. 65

    Meyer, H. S. et al. Cell type-specific thalamic innervation in a column of rat vibrissal cortex. Cereb. Cortex 20, 2287–2303 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66

    Ohno, S. et al. A morphological analysis of thalamocortical axon fibers of rat posterior thalamic nuclei: a single neuron tracing study with viral vectors. Cereb. Cortex 22, 2840–2857 (2012).

    Article  PubMed  Google Scholar 

  67. 67

    Lu, S. M. & Lin, R. C. Thalamic afferents of the rat barrel cortex: a light- and electron-microscopic study using Phaseolus vulgaris leucoagglutinin as an anterograde tracer. Somatosens. Mot. Res. 10, 1–16 (1993).

    CAS  Article  PubMed  Google Scholar 

  68. 68

    Mease, R. A., Metz, M. & Groh, A. Cortical sensory responses are enhanced by the higher-order thalamus. Cell Rep. 14, 208–215 (2016).

    CAS  Article  PubMed  Google Scholar 

  69. 69

    Groh, A. et al. Convergence of cortical and sensory driver inputs on single thalamocortical cells. Cereb. Cortex 24, 3167–3179 (2014).

    Article  PubMed  Google Scholar 

  70. 70

    Ahissar, E., Sosnik, R. & Haidarliu, S. Transformation from temporal to rate coding in a somatosensory thalamocortical pathway. Nature 406, 302–306 (2000).

    CAS  Article  PubMed  Google Scholar 

  71. 71

    Moore, J. D., Mercer Lindsay, N., Deschênes, M. & Kleinfeld, D. Vibrissa self-motion and touch are reliably encoded along the same somatosensory pathway from brainstem through thalamus. PLoS Biol. 13, e1002253 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Guo, Z. V. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. 73

    Kwon, S. E., Yang, H., Minamisawa, G. & O'Connor, D. H. Sensory and decision-related activity propagate in a cortical feedback loop during touch perception. Nat. Neurosci. 19, 1243–1249 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  74. 74

    Self, M., Kooijmans, R. N., Supèr, H., Lamme, V. A. F. & Roelfsema, P. R. Different glutamate receptors convey feedforward and recurrent processing in macaque V1. Proc. Natl Acad. Sci. USA 109, 11031–11036 (2012).

    CAS  Article  PubMed  Google Scholar 

  75. 75

    Daw, N. W., Stein, P. S. G. & Fox, K. The role of NMDA receptors in information processing. Annu. Rev. Neurosci. 16, 207–222 (1993).

    CAS  Article  PubMed  Google Scholar 

  76. 76

    Rivadulla, C., Martinez, L. M., Varela, C. & Cudeiro, J. Completing the corticofugal loop: a visual role for the corticogeniculate type 1 metabotropic glutamate receptor. J. Neurosci. 22, 2956–2962 (2002).

    CAS  Article  PubMed  Google Scholar 

  77. 77

    Gambino, F. et al. Sensory-evoked LTP driven by dendritic plateau potentials in vivo. Nature 515, 116–119 (2014).

    CAS  Article  PubMed  Google Scholar 

  78. 78

    Klink, P. C., Dagnino, B., Gariel-Mathis, M. A. & Roelfsema, P. R. Distinct feedforward and feedback effects of microstimulation in visual cortex reveal neural mechanisms of texture segregation. Neuron 95, 209–220 (2017).

    CAS  Article  PubMed  Google Scholar 

  79. 79

    Tritsch, N. X. & Sabatini, B. L. Dopaminergic modulation of synaptic transmission in cortex and striatum. Neuron 76, 33–50 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  80. 80

    Picciotto, M. R., Higley, M. J. & Mineur, Y. S. Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior. Neuron 76, 116–129 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  81. 81

    Eggermann, E., Kremer, Y., Crochet, S. & Petersen, C. C. H. Cholinergic signals in mouse barrel cortex during active whisker sensing. Cell Rep. 9, 1654–1660 (2014).

    CAS  Article  PubMed  Google Scholar 

  82. 82

    Gu, Q. Neuromodulatory transmitter system in the cortex and their role in cortical plasticity. Neuroscience 111, 814–835 (2002).

    Article  Google Scholar 

  83. 83

    Lesch, K. P. & Waider, J. Serotonin in the modulation of neural plasticity and networks: implications for neurodevelopmental disorders. Neuron 76, 175–191 (2012).

    CAS  Article  PubMed  Google Scholar 

  84. 84

    Froemke, R. C. Plasticity of cortical excitatory-inhibitory balance. Annu. Rev. Neurosci. 38, 195–219 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  85. 85

    Hayashi-takagi, A. et al. Labelling and optical erasure of synaptic memory traces in the motor cortex. Nature 525, 333–338 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  86. 86

    Xiong, Q., Znamenskiy, P. & Zador, A. M. Selective corticostriatal plasticity during acquisition of an auditory discrimination task. Nature 521, 348–351 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  87. 87

    Jin, X. & Costa, R. M. Start/stop signals emerge in nigrostriatal circuits during sequence learning. Nature 466, 457–462 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  88. 88

    Liu, B., Huberman, A. D. & Scanziani, M. Cortico-fugal output from visual cortex promotes plasticity of innate motor behaviour. Nature 538, 383–387 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  89. 89

    Schoups, A., Vogels, R., Qian, N. & Orban, G. A. Practising orientation identification improves orientation coding in V1 neurons. Nature 412, 549–553 (2001).

    CAS  Article  PubMed  Google Scholar 

  90. 90

    Poort, J. et al. Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron 86, 1478–1490 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  91. 91

    Goltstein, P. M., Coffey, E. B. J., Roelfsema, P. R. & Pennartz, C. M. A. In vivo two-photon Ca2+ imaging reveals selective reward effects on stimulus-specific assemblies in mouse visual cortex. J. Neurosci. 33, 11540–11555 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  92. 92

    Freedman, D. J. & Assad, J. A. Experience dependent representation of visual categories in parietal cortex. Nature 443, 85–88 (2006).

    CAS  Article  PubMed  Google Scholar 

  93. 93

    Makino, H. & Komiyama, T. Learning enhances the relative impact of top-down processing in the visual cortex. Nat. Neurosci. 18, 1116–1122 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  94. 94

    Bajo, V. M., Nodal, F. R., Moore, D. R. & King, A. J. The descending corticocollicular pathway mediates learning-induced auditory plasticity. Nat. Neurosci. 13, 253–260 (2010).

    CAS  Article  PubMed  Google Scholar 

  95. 95

    Brosch, T., Neumann, H. & Roelfsema, P. R. Reinforcement learning of linking and tracing contours in recurrent neural networks. PLoS Comput. Biol. 11, e1004489 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 96

    Pooresmaeili, A., Poort, J. & Roelfsema, P. R. Simultaneous selection by object-based attention in visual and frontal cortex. Proc. Natl Acad. Sci. USA 111, 6467–6472 (2014).

    CAS  Article  PubMed  Google Scholar 

  97. 97

    Self, M. W. et al. The effects of context and attention on spiking activity in human early visual cortex. PLoS Biol. 14, e1002420 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98

    Houtkamp, R., Spekreijse, H. & Roelfsema, P. R. A gradual spread of attention during mental curve tracing. Percept. Psychophys. 65, 1136–1144 (2003).

    CAS  Article  PubMed  Google Scholar 

  99. 99

    Roelfsema, P. R. & Spekreijse, H. The representation of erroneously perceived stimuli in the primary visual cortex. Neuron 31, 853–863 (2001).

    CAS  Article  PubMed  Google Scholar 

  100. 100

    Khayat, P. S., Pooresmaeili, A. & Roelfsema, P. R. Time course of attentional modulation in the frontal eye field during curve tracing. J. Neurophysiol. 101, 1813–1822 (2009).

    CAS  Article  PubMed  Google Scholar 

  101. 101

    Roelfsema, P. R. Cortical algorithms for perceptual grouping. Annu. Rev. Neurosci. 29, 203–227 (2006).

    CAS  Article  PubMed  Google Scholar 

  102. 102

    Reynolds, J. H. & Chelazzi, L. Attentional modulation of visual processing. Annu. Rev. Neurosci. 27, 611–647 (2004).

    CAS  Article  PubMed  Google Scholar 

  103. 103

    Zhou, H., Schafer, R. J. & Desimone, R. Pulvinar–cortex interactions in vision and attention. Neuron 89, 209–220 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  104. 104

    Purushothaman, G., Marion, R., Li, K. & Casagrande, V. A. Gating and control of primary visual cortex by pulvinar. Nat. Neurosci. 15, 905–912 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  105. 105

    Robinson, D. L. & Petersen, S. E. The pulvinar and visual salience. Trends Neurosci. 15, 127–132 (1992).

    CAS  Article  PubMed  Google Scholar 

  106. 106

    Chalupa, L. M., Coyle, R. S. & Lindsley, D. B. Effect of pulvinar lesions on visual pattern discrimination in monkeys. J. Neurophysiol. 39, 354–369 (1976).

    CAS  Article  PubMed  Google Scholar 

  107. 107

    Manita, S. et al. A top-down cortical circuit for accurate sensory perception. Neuron 86, 1304–1316 (2015).

    CAS  Article  PubMed  Google Scholar 

  108. 108

    Xu, N. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).

    CAS  Article  PubMed  Google Scholar 

  109. 109

    Sachidhanandam, S., Sreenivasan, V., Kyriakatos, A., Kremer, Y. & Petersen, C. C. H. Membrane potential correlates of sensory perception in mouse barrel cortex. Nat. Neurosci. 16, 1671–1677 (2013).

    CAS  Article  PubMed  Google Scholar 

  110. 110

    Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016).

    CAS  Article  PubMed  Google Scholar 

  111. 111

    Gambino, F. & Holtmaat, A. Spike-timing-dependent potentiation of sensory surround in the somatosensory cortex is facilitated by deprivation-mediated disinhibition. Neuron 75, 490–502 (2012).

    CAS  Article  PubMed  Google Scholar 

  112. 112

    Letzkus, J. J. et al. A disinhibitory microcircuit for associative learning in the auditory cortex. Nature 480, 331–335 (2011).

    CAS  Article  PubMed  Google Scholar 

  113. 113

    Letzkus, J. J., Wolff, S. B. E. & Lüthi, A. Disinhibition, a circuit mechanism for associative learning and memory. Neuron 88, 264–276 (2015).

    CAS  Article  PubMed  Google Scholar 

  114. 114

    Lee, S., Hjerling-Leffler, J., Zagha, E., Fishell, G. & Rudy, B. The largest group of superficial neocortical GABAergic interneurons expresses ionotropic serotonin receptors. J. Neurosci. 30, 16796–16808 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  115. 115

    Wall, N. R. et al. Brain-wide maps of synaptic input to cortical interneurons. J. Neurosci. 36, 4000–4009 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  116. 116

    Audette, N. J., Urban-Ciecko, J., Matsushita, M. & Barth, A. L. POm thalamocortical input drives layer-specific microcircuits in somatosensory cortex. Cereb. Cortex https://doi.org/10.1093/cercor/bhx044 (2017).

    Article  Google Scholar 

  117. 117

    Pi, H.-J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  118. 118

    Fu, Y. et al. A cortical circuit for gain control by behavioral state. Cell 156, 1139–1152 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  119. 119

    Pfeffer, C. K., Xue, M., He, M., Huang, Z. J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  120. 120

    Wang, Y. et al. Anatomical, physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat. J. Physiol. 561, 65–90 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  121. 121

    van Versendaal, D. et al. Elimination of inhibitory synapses is a major component of adult ocular dominance plasticity. Neuron 74, 374–383 (2012).

    CAS  Article  PubMed  Google Scholar 

  122. 122

    Kubota, Y., Hatada, S., Kondo, S., Karube, F. & Kawaguchi, Y. Neocortical inhibitory terminals innervate dendritic spines targeted by thalamocortical afferents. J. Neurosci. 27, 1139–1150 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  123. 123

    Palmer, L., Murayama, M. & Larkum, M. Inhibitory regulation of dendritic activity in vivo. Front. Neural Circuits 6, 26 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  124. 124

    Chen, S. X., Kim, A. N., Peters, A. J. & Komiyama, T. Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning. Nat. Neurosci. 18, 1109–1115 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  125. 125

    Cichon, J. & Gan, W. Branch-specific dendritic Ca2+ spikes cause persistent synaptic plasticity. Nature 520, 180–185 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  126. 126

    Fu, Y., Kaneko, M. K., Tang, Y., Alvarez-Buylla, A. & Stryker, M. P. A cortical disinhibitory circuit for enhancing adult plasticity. eLife 4, e05558 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. 127

    Higley, M. J. Localized GABAergic inhibition of dendritic Ca2+ signalling. Nat. Rev. Neurosci. 15, 567–572 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  128. 128

    Bittner, K. C., Milstein, A. D., Grienberger, C., Romani, S. & Magee, J. C. Behavioral time scale synaptic plasticity underlies CA1 place fields. Science 357, 1033–1036 (2017).

    CAS  Article  PubMed  Google Scholar 

  129. 129

    Basu, J. et al. Gating of hippocampal activity, plasticity, and memory by entorhinal cortex long-range inhibition. Science 351, aaa5694 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. 130

    Smith, S. L., Smith, I. T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. Nature 503, 115–120 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  131. 131

    Dahmen, J. C., Hartley, D. E. H. & King, A. J. Stimulus-timing-dependent plasticity of cortical frequency representation. J. Neurosci. 28, 13629–13639 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  132. 132

    Pawlak, V. & Kerr, J. N. D. Dopamine receptor activation is required for corticostriatal spike-timing-dependent plasticity. J. Neurosci. 28, 2435–2446 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  133. 133

    Pawlak, V., Greenberg, D. S., Sprekeler, H., Gerstner, W. & Kerr, J. N. D. Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo. eLife 2, e00012 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  134. 134

    Haas, H. L., Sergeeva, O. A. & Selbach, O. Histamine in the central nervous system. Physiol. Rev. 88, 1183–1241 (2008).

    CAS  Article  PubMed  Google Scholar 

  135. 135

    Montague, P. R., Hyman, S. E. & Cohen, J. D. Computational roles for dopamine in behavioral control. Nature 431, 760–767 (2004).

    CAS  Article  PubMed  Google Scholar 

  136. 136

    Schultz, W. Multiple dopamine functions at different time courses. Annu. Rev. Neurosci. 30, 259–288 (2007).

    CAS  Article  PubMed  Google Scholar 

  137. 137

    Bromberg-Martin, E. S., Matsumoto, M. & Hikosaka, O. Dopamine in motivational control: rewarding, aversive, and alerting. Neuron 68, 815–834 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  138. 138

    Bao, S., Chan, V. T. & Merzenich, M. M. Cortical remodelling induced by activity of ventral tegmental dopamine neurons. Nature 412, 79–81 (2001).

    CAS  Article  PubMed  Google Scholar 

  139. 139

    Lammel, S. et al. Input-specific control of reward and aversion in the ventral tegmental area. Nature 491, 212–217 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  140. 140

    Zaborszky, L. et al. Neurons in the basal forebrain project to the cortex in a complex topographic organization that reflects corticocortical connectivity patterns: an experimental study based on retrograde tracing and 3D reconstruction. Cereb. Cortex 25, 118–137 (2015).

    Article  PubMed  Google Scholar 

  141. 141

    Kim, J.-H. et al. Selectivity of neuromodulatory projections from the basal forebrain and locus ceruleus to primary sensory cortices. J. Neurosci. 36, 5314–5327 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  142. 142

    Kawai, H., Lazar, R. & Metherate, R. Nicotinic control of axon excitability regulates thalamocortical transmission. Nat. Neurosci. 10, 1168–1175 (2007).

    CAS  Article  PubMed  Google Scholar 

  143. 143

    Férézou, I. et al. 5-HT3 receptors mediate serotonergic fast synaptic excitation of neocortical vasoactive intestinal peptide/cholecystokinin interneurons. J. Neurosci. 22, 7389–7397 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  144. 144

    Pinto, L. et al. Fast modulation of visual perception by basal forebrain cholinergic neurons. Nat. Neurosci. 16, 1857–1863 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  145. 145

    Hangya, B., Ranade, S. P., Lorenc, M. & Kepecs, A. Central cholinergic neurons are rapidly recruited by reinforcement feedback. Cell 162, 1155–1168 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  146. 146

    Richardson, R. T. & DeLong, M. R. Nucleus basalis of Meynert neuronal activity during a delayed response task in monkey. Brain Res. 399, 364–368 (1986).

    CAS  Article  PubMed  Google Scholar 

  147. 147

    Chubykin, A. A., Roach, E. B., Bear, M. F. & Shuler, M. G. H. A cholinergic mechanism for reward timing within primary visual cortex. Neuron 77, 723–735 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  148. 148

    Kilgard, M. P. & Merzenich, M. M. Cortical map reorganization enabled by nucleus basalis activity. Science 279, 1714–1718 (1998).

    CAS  Article  PubMed  Google Scholar 

  149. 149

    Froemke, R. C. et al. Long-term modification of cortical synapses improves sensory perception. Nat. Neurosci. 16, 79–88 (2013).

    CAS  Article  PubMed  Google Scholar 

  150. 150

    Bakin, J. S. & Weinberger, N. M. Induction of a physiological memory in the cerebral cortex by stimulation of the nucleus basalis. Proc. Natl Acad. Sci. USA 93, 11219–11224 (1996).

    CAS  Article  PubMed  Google Scholar 

  151. 151

    Froemke, R. C., Merzenich, M. M. & Schreiner, C. E. A synaptic memory trace for cortical receptive field plasticity. Nature 450, 425–429 (2007).

    CAS  Article  PubMed  Google Scholar 

  152. 152

    Juliano, S. L., Ma, W. & Eslin, D. Cholinergic depletion prevents expansion of topographic maps in somatosensory cortex. Proc. Natl Acad. Sci. USA 88, 780–784 (1991).

    CAS  Article  PubMed  Google Scholar 

  153. 153

    Warburton, E. C. et al. Cholinergic neurotransmission is essential for perirhinal cortical plasticity and recognition memory. Neuron 38, 987–996 (2003).

    CAS  Article  PubMed  Google Scholar 

  154. 154

    Easton, A., Ridley, R. M., Baker, H. F. & Gaffan, D. Unilateral lesions of the cholinergic basal forebrain and fornix in one hemisphere and inferior temporal cortex in the opposite hemisphere produce severe learning impairements in rhesus monkeys. Cereb. Cortex 12, 729–736 (2002).

    CAS  Article  PubMed  Google Scholar 

  155. 155

    Winkler, J., Suhr, S. T., Gage, F. H., Thal, L. J. & Fisher, L. J. Essential role of neocortical acetylcholine in spatial memory. Nature 375, 484–487 (1995).

    CAS  Article  PubMed  Google Scholar 

  156. 156

    Jacobs, B. L. & Azmitia, E. C. Structure and function of the brain serotonin system. Physiol. Rev. 72, 165–229 (1992).

    CAS  Article  PubMed  Google Scholar 

  157. 157

    Celada, P., Puig, M. V. & Artigas, F. Serotonin modulation of cortical neurons and networks. Front. Integr. Neurosci. 7, 25 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  158. 158

    Nakamura, K., Matsumoto, M. & Hikosaka, O. Reward-dependent modulation of neuronal activity in the primate dorsal raphe nucleus. J. Neurosci. 28, 5331–5343 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  159. 159

    Ranade, S. P. & Mainen, Z. F. Transient firing of dorsal raphe neurons encodes diverse and specific sensory, motor, and reward events. J. Neurophysiol. 102, 3026–3037 (2009).

    Article  PubMed  Google Scholar 

  160. 160

    Cohen, J. Y., Amoroso, M. W. & Uchida, N. Serotonergic neurons signal reward and punishment on multiple timescales. eLife 4, e06346 (2015).

    Article  CAS  PubMed Central  Google Scholar 

  161. 161

    Bromberg-martin, E. S., Hikosaka, O. & Nakamura, K. Coding of task reward value in the dorsal raphe nucleus. J. Neurosci. 30, 6262–6272 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  162. 162

    Liu, Z. et al. Dorsal raphe neurons signal reward through 5-HT and glutamate. Neuron 81, 1360–1374 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  163. 163

    Nakamura, K. The role of the dorsal raphé nucleus in reward-seeking behavior. Front. Integr. Neurosci. 7, 60 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  164. 164

    Jitsuki, S. et al. Serotonin mediates cross-modal reorganization of cortical circuits. Neuron 69, 780–792 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  165. 165

    Sara, S. J. & Bouret, S. Orienting and reorienting: the locus coeruleus mediates cognition through arousal. Neuron 76, 130–141 (2012).

    CAS  Article  PubMed  Google Scholar 

  166. 166

    Bouret, S. & Richmond, B. J. Sensitivity of locus ceruleus neurons to reward value for goal-directed actions. J. Neurosci. 35, 4005–4014 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  167. 167

    Martins, A. R. O. & Froemke, R. C. Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex. Nat. Neurosci. 18, 1483–1492 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  168. 168

    Devilbiss, D. M. & Waterhouse, B. D. Phasic and tonic patterns of locus coeruleus output differentially modulate sensory network function in the awake rat. J. Neurophysiol. 105, 69–87 (2011).

    Article  PubMed  Google Scholar 

  169. 169

    Pawlak, V., Wickens, J. R., Kirkwood, A. & Kerr, J. N. D. Timing is not everything: neuromodulation opens the STDP gate. Front. Synapt. Neurosci. 2, 146 (2010).

    Article  Google Scholar 

  170. 170

    Hu, H. et al. Emotion enhances learning via norepinephrine regulation of AMPA-receptor trafficking. Cell 131, 160–173 (2007).

    CAS  Article  PubMed  Google Scholar 

  171. 171

    Johansen, J. P. et al. Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation. Proc. Natl Acad. Sci. 111, E5584–E5592 (2014).

    CAS  Article  PubMed  Google Scholar 

  172. 172

    Urbanczik, R. & Senn, W. Reinforcement learning in populations of spiking neurons. Nat. Neurosci. 12, 250–252 (2009).

    CAS  Article  PubMed  Google Scholar 

  173. 173

    Seol, G. H. et al. Neuromodulators control the polarity of spike-timing-dependent plasticity. Neuron 55, 919–929 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  174. 174

    He, K. et al. Distinct eligibility traces for LTP and LTD in cortical synapses. Neuron 88, 528–538 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  175. 175

    Cassenaer, S. & Laurent, G. Conditional modulation of spike-timing-dependent plasticity for olfactory learning. Nature 482, 47–52 (2012).

    CAS  Article  PubMed  Google Scholar 

  176. 176

    Redondo, R. L. & Morris, R. G. M. Making memories last: the synaptic tagging and capture hypothesis. Nat. Rev. Neurosci. 12, 17–30 (2011).

    CAS  Article  PubMed  Google Scholar 

  177. 177

    Clopath, C., Ziegler, L., Vasilaki, E., Büsing, L. & Gerstner, W. Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS Comput. Biol. 4, e1000248 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. 178

    Nadim, F. & Bucher, D. Neuromodulation of neurons and synapses. Curr. Opin. Neurobiol. 29, 48–56 (2014).

    CAS  Article  PubMed  Google Scholar 

  179. 179

    Blundon, J. A., Bayazitov, I. T. & Zakharenko, S. S. Presynaptic gating of postsynaptically expressed plasticity at mature thalamocortical synapses. J. Neurosci. 31, 16012–16025 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  180. 180

    Brzosko, Z., Schultz, W. & Paulsen, O. Retroactive modulation of spike timing-dependent plasticity by dopamine. eLife 4, e09685 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  181. 181

    Güçlü, U. & van Gerven, M. A. J. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005–10014 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. 182

    Urbanczik, R. & Senn, W. Learning by the dendritic prediction of somatic spiking. Neuron 81, 521–528 (2014).

    CAS  Article  PubMed  Google Scholar 

  183. 183

    Schiess, M., Urbanczik, R. & Senn, W. Somato-dendritic synaptic plasticity and error-backpropagation in active dendrites. PLoS Comput. Biol. 12, e1004638 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. 184

    Scellier, B. & Bengio, Y. Equilibrium propagation: bridging the gap between energy-based models and backpropagation. Front. Comput. Neurosci. 11, 24 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  185. 185

    Marblestone, A., Wayne, G. & Kording, K. Towards an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10, 94 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  186. 186

    Laramée, M.-E. & Boire, D. Visual cortical areas of the mouse: comparison of parcellation and network structure with primates. Front. Neural Circuits 8, 149 (2015).

    PubMed  PubMed Central  Google Scholar 

  187. 187

    Berezovskii, V. K., Nassi, J. J. & Born, R. T. Segregation of feedforward and feedback projections in mouse visual cortex. J. Comp. Neurol. 519, 3672–3683 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  188. 188

    Agnati, L. F., Guidolin, D., Guescini, M., Genedani, S. & Fuxe, K. Understanding wiring and volume transmission. Brain Res. Rev. 64, 137–159 (2010).

    Article  PubMed  Google Scholar 

  189. 189

    Knott, G. W., Quairiaux, C., Genoud, C. & Welker, E. Formation of dendritic spines with GABAergic synapses by whisker stimulation in induced adult mice. Neuron 34, 265–273 (2002).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank S. Bohte, C. Pennartz, M. Sherman, V. Kehayas and H. Kennedy for helpful input and comments. The work was supported by the Netherlands Organisation for Scientific Research (NWO; ALW grant 823-02-010 to P.R.R.), the European Union Seventh Framework Programme (grant agreement 7202070 'Human Brain Project' to P.R.R. and European Research Council (ERC) grant agreement 339490 'Cortic_al_gorithms' to P.R.R.), the Swiss National Science Foundation (SNF; research grants 31003A-153448 and CRSII3-154453 to A.H. and the National Centre of Competence in Research (NCCR) SYNAPSY grant 51NF40-158776 to A.H.) and the International Foundation for Research in Paraplegia (to A.H.).

Author information

Affiliations

Authors

Contributions

P.R.R. and A.H. researched data for the article, made substantial contributions to discussions of the content, wrote the article and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Pieter R. Roelfsema.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

PowerPoint slides

Glossary

Reward-prediction errors

(RPEs). Differences between the amount of reward that was expected and the amount that was obtained.

Reinforcement learning

Trial-and-error learning when interacting with an environment and experiencing rewards and punishments as consequences of the chosen actions.

Eligibility traces

Local parameters at the synapses of a network that determine whether they undergo plasticity upon reward-prediction errors during reinforcement learning.

Synaptic tags

Biochemical signals at synapses that determine whether they will undergo plasticity.

Error-backpropagation rule

A mathematical method used to calculate the contribution of connections to the error of a network with multiple layers between input and output.

Derivatives

The derivative of the error function to a synaptic weight is the rate of change of the error when changing the strength of a particular synapse.

Gradient descent

A mathematical optimization method that determines the direction of the vector of changes in all synaptic weights that causes the largest decrease in the error of the network.

Translation invariant

A property of an image processing system whereby the recognition of the object is independent of the object's location relative to the viewer.

Feedback alignment

A process in which, if the feedforward and feedback weights of a neural network are not reciprocal, error backpropagation causes feedforward weights to align; that is, to become more symmetrical.

Optokinetic reflex

The innate reflexive smooth eye movements elicited by large moving visual stimuli.

Frontal eye fields

Area of the frontal cortex involved in the planning of eye movements.

Martinotti cells

Somatostatin-expressing inhibitory interneurons with a characteristic morphology that target the dendritic tufts of pyramidal cells in various cortical layers.

Unsupervised learning

A type of learning in which the structure of unlabelled data is inferred as information about desired categorization is not provided.

Spike-timing-dependent plasticity

(STDP). A plasticity rule whereby the change in the strength of synapses depends on the relative timing of presynaptic and postsynaptic action potentials.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Roelfsema, P., Holtmaat, A. Control of synaptic plasticity in deep cortical networks. Nat Rev Neurosci 19, 166–180 (2018). https://doi.org/10.1038/nrn.2018.6

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing